The US Department of Health and Human Services has renewed its "war on cancer" by declaring the elimination of race and age disparities in cancer screening and management as a key public health priority for the next century; the National Cancer Institute Year 2010 goals echo this priority. The importance of these cancer control objectives is cast on the backdrop of the changing demographic profile of the US: by the year 2030, one in five women will be 65 years or older, and 40 percent will be from minority groups. Thus, successful achievement of these objectives will require application of effective interventions to diverse populations, and integration of evolving paradigms of breast cancer care into public health initiatives. Modeling can evaluate the success of such initiatives. However, the majority of existing models have focused on a single dimension of breast cancer care, and generally lack flexibility to study trends in outcomes among population subgroups. To address this gap, Lombardi Cancer Center, in collaboration with MEDTAP International, has constituted a multi-disciplinary team of demographers, epidemiologists, oncologists, genetics, behavioral science, and health services researchers, and economists to develop a novel discrete-event, stochastic population forecasting simulation model. Our overarching goal is to extend and use our existing model to develop an integrated model of disease history linked to sub-models portraying modifiable points in the cancer control process, including primary prevention, early detection, methods to enhance diagnosis, and improvements in treatment quality and practice (ie, models within a model). We will use this model to evaluate the impact of changes in behaviors, practice patterns, and interventions on intermediate outcomes and incidence and mortality trends. Innovative features of our model include the integration of epidemiological and biological representations of the disease process with the screening, diagnostic, and treatment, portrayal of disease in Whites and Blacks, and incorporation of the effects of comorbidities on effectiveness and quality-adjusted survival. We will test hypotheses about which services will be most effective, in which population-, age-, and health-groups, for which phase of care, in reducing overall breast cancer mortality. Secondary objectives include using existing utility data to identify areas where preferences change conclusions about effectiveness, and o use cost data to evaluate which strategies yield the maximal improvement in outcomes at the most reasonable costs. Overall, data from the model will provide a framework for setting cancer control priorities in the next century.